Labeless approach becomes a recent trend due to the nature of abnormal data, which is expensive to collect. Clustering[1]}, [2]}, [3]}, [4]}, SVM network[5]} and isolation forest[6]} are ways to perform anomaly detection without labels.
Because normal data is relatively easy and inexpensive to collect, training only with normal data become one branch, which called novelty detection.
Adversarially Learned One-Class Classifier[7]} and Old is Gold (OGNet) [8]} are GAN(Generative Adversarial Net)[9]} based architectures, which use discriminator for anomaly detection. During the training, the adversarial model trained on one-class, and inference time, generator and discriminator co-work for anomalous-classification. Deep-cascade net[10]} is also trained with normal data to learn the Gaussian model during the training and use this model for anomaly detection in each step of the deep network.
These labeless, unsupervised learning approaches are more robust to an unknown anomaly compared to supervised learning.
However, they have an opposite problem of supervised learning approaches, which is hard to tell anomaly data near to the normal data, not as accurate as supervised learning in detecting known anomalies. OGNet attempts to specifically handle this issue by generating psuedo anomalies using an old state of the generator however, the overall training still utilizes only normal data.
